def get_current_shares():
"""Gets current shares, writes updated version back to json"""
company_dict = get_company_dict()
gmt = datetime.now()
market_time = gmt.replace(hour=abs(gmt.hour - 5))
# Ensure market is open (opening hours 9:30 - 4 EST)
if int(str(market_time.hour) + str(market_time.minute if market_time.minute >= 10 else f'0{market_time.minute}')):
for company in company_dict:
try:
stock = Pinance(company_dict[company]["symbol"])
stock.get_quotes()
share = stock.quotes_data["regularMarketPrice"]
# Gets the current share price, replaces the "current"
# and adds to the sharePriceList
curr_day = str(company_dict[company]["day"])
company_dict[company]["currentSharePrice"] = float(share)
company_dict[company]["sharePriceList"][curr_day].append(("{:%d-%m-%Y %H:%M:%S}".format(datetime.now()),
float(share)))
# Gets the current share change
share_change = 1.0 - (company_dict[company]["initialSharePrice"] /
company_dict[company]["currentSharePrice"])
company_dict[company]["shareChange"] = share_change
except TypeError as error:
# Will catch the error if share returns a value other than a float
logging.debug(error)
with open(MONITOR, "w") as f:
json.dump(company_dict, f, sort_keys=True, indent=4, ensure_ascii=False)
async def stock(symbol: str):
symbol = symbol.upper()
if symbol == "HELP":
await bot.say(
'Command is written as: ",stock <Stock>". Bot only can take input of stock symbol such as AAPL or GOOGL. To get data on Apple one would type ",crypto AAPL".'
)
else:
try:
company = Pinance(symbol)
company.get_quotes()
stock = company.quotes_data
price = stock['regularMarketPrice']
percentchange = stock['regularMarketChangePercent']
index = stock['fullExchangeName']
lastupdate = stock['regularMarketTime']
timeupdated = datetime.datetime.fromtimestamp(
int(lastupdate)).strftime('%Y-%m-%d %H:%M:%S')
await bot.say(symbol + ' is currently trading at $' + str(price) +
' corresponding to a ' + str(percentchange) +
'% change. This was last updated at ' + timeupdated)
except KeyError:
await bot.say('Error: This input is not a stock symbol.')
def get_initial_company_info():
"""Gets the initial information for each company"""
company_dict = get_company_dict()
for company in company_dict:
# Gets symbol for company
if company_dict[company]["symbol"] == "unknown":
try:
with urllib.request.urlopen(
f'https://finance.yahoo.com/_finance_doubledown/'
f'api/resource/searchassist;searchTerm={company_dict[company]["companyName"]}') as response:
html = response.read().decode()
d = json.loads(html)
company_dict[company]["symbol"] = d['items'][0]['symbol']
except urllib.error.HTTPError as error:
logging.debug(error)
break
# Gets initial share price
if company_dict[company]["initialSharePrice"] == 1:
stock = Pinance(company_dict[company]["symbol"])
stock.get_quotes()
share = stock.quotes_data["regularMarketPrice"]
company_dict[company]["initialSharePrice"] = float(share)
company_dict[company]["currentSharePrice"] = float(share)
with open(MONITOR, "w") as f:
json.dump(company_dict, f, sort_keys=True, indent=4, ensure_ascii=False)
def get_quote(symbol):
stock = Pinance(symbol)
stock.get_quotes()
rec_data = stock.quotes_data
print(rec_data)
last_tradeprice = rec_data['LastTradePrice']
print('LTP : %s"' %last_tradeprice)
def liveDailyPercent(symbol):
stock = Pinance(symbol)
stock.get_quotes()
percentChange = stock.quotes_data['regularMarketChangePercent']
cacheQuote(symbol,{'percentChange':percentChange,
'lastPrice':stock.quotes_data['regularMarketPrice'],
'change':stock.quotes_data['regularMarketChange']})
return percentChange
def get_forward_pe_ratio(ticker):
"""
Stock Price / Future Earnings Per Share
:param ticker:
:return: forward PE Ratio
"""
from pinance import Pinance
pinance_data = Pinance(ticker)
pinance_data.get_quotes()
pinance_data.quotes_data['forwardPE']
def liveQuote(symbol, lock=None):
stock = Pinance(symbol)
stock.get_quotes()
if 'postMarketPrice' in stock.quotes_data.keys():
currValue = float(stock.quotes_data['postMarketPrice'])*STOCKS[symbol]
dayChange = float(stock.quotes_data['regularMarketChange'] + stock.quotes_data['postMarketChange'])*STOCKS[symbol]
else:
currValue = float(stock.quotes_data['regularMarketPrice'])*STOCKS[symbol]
dayChange = stock.quotes_data['regularMarketChange']*STOCKS[symbol]
cacheQuote(symbol,
{'lastPrice':stock.quotes_data['regularMarketPrice'],
'change':stock.quotes_data['regularMarketChange'],
'percentChange':stock.quotes_data['regularMarketChangePercent']},
lock)
return (currValue,dayChange)
def get_stock_data(
tickers=get_sp500_tickers(), path='pinance_data/{}.pickle',
update=False):
for symbol in tickers:
if update:
print("Reading data for ticker " + symbol['ticker'])
stock = Pinance(symbol['ticker'])
stock.get_quotes()
stock.get_news()
stock_data = stock.quotes_data
stock_news = stock.news_data
data = {
'ticker': symbol['ticker'],
'data': stock_data,
'news': stock_news
}
write_to_pickle(data, path.format(symbol['ticker']))
else:
with open(path.format(symbol['ticker']), 'rb') as handle:
data = pickle.load(handle)
yield data
# my_url = 'http://topforeignstocks.com/stock-lists/the-complete-list-of-biotech-stocks-trading-on-nasdaq/' # List of biotech companies (outdated source)
# uClient = requests.get(my_url) #downloads webpage
# page_html = uClient.text
# page_soup = soup(page_html, "html.parser")
# bio_tech_companies = page_soup.findAll("td",{"class":"column-2"}) # specific to name
# bio_tech_companies_symbol = page_soup.findAll("td",{"class":"column-3"}) # specific to symbol
NASDAQ_File = pd.read_csv('NASDAQ_Company_List.csv')
Industry = NASDAQ_File['Industry']
Symbol = NASDAQ_File['Symbol']
Name = NASDAQ_File['Name']
for i in range(len(Industry)):
if 'Biotechnology' in str(Industry[i]) or 'Pharmaceuticals' in str(
Industry[i]):
query = str(Name[i]) # gets name of biotech company
query_symbol = str(Symbol[i]) # gets symbol of biotech company
stock = Pinance(query_symbol)
stock.get_quotes()
try: # necessary if stock.get_quotes returns an empty or incomplete dictionary
if stock.quotes_data['tradeable'] == True:
stock_price = ((stock.quotes_data['regularMarketPrice']))
if stock_price < 35.00:
query_params = {
"q":
"language:english site_type:news "
"title: {0}".format(query),
"sort":
"crawled",
}
output = webhoseio.query("filterWebContent", query_params)
count = 0 # used to limit number of articles printed on a certain company
for i in range(len(output['posts'])):
#TODO Create a `PinanceClient` class that has functions in it that call the pinance code
# TODO this needs to be a function that takes in a list of stocks and puts the quotes into a dictionay
"""
{'VZ':
{
'stock': ...,
'quote': ...,
'market_price': ...
},
...
}
"""
for each_symbol in dogs_of_dow:
stock_list = Pinance(each_symbol)
symbol = each_symbol
stock = Pinance(symbol)
stock.get_quotes()
# Gets the price of the list of stocks in djia_list.
price_stock = stock.quotes_data['regularMarketPrice']
# Gets the trailing annual divident of the list of stocks in djia_list.
dividend_yield = stock.quotes_data['trailingAnnualDividendRate']
# Prints stock tickers, prices, and dif yields of the list of stocks in djia_list.
#print(each_symbol, price_stock, dividend_yield)
# Calculate number of shares to buy based on stock allocation and the price of each stock in dog list.
shares_to_buy = stock_allocation / price_stock
from pinance import Pinance symbol = "OGEN" stock = Pinance(symbol) stock.get_quotes() print((stock.quotes_data))
alldata = []
try:
for stock in static.equitties_train:
mydata = quandl.get_table('ZACKS/MKTV', ticker=stock)
if len(mydata) > 0:
mydata['date'] = pd.to_datetime(mydata['per_end_date'])
mc = mydata.set_index('date')[['mkt_val'
]].sort_index().iloc[-1]['mkt_val']
alldata.append((stock, round(mc)))
else:
alldata.append((stock, 'nan'))
except:
print(alldata)
#this is the good one
from pinance import Pinance
alldata = []
try:
for stock in static.equitties_train:
share = Pinance(stock)
try:
share.get_quotes()
mc = share.quotes_data['marketCap']
except:
alldata.append((stock, 'nan'))
continue
alldata.append((stock, round(mc / 1000000)))
except:
print(alldata)
from pinance import Pinance
symbol = "VALO.BA"
valo = Pinance("VALO.BA")
edn = Pinance("EDN.BA")
agro = Pinance("AGRO.BA")
valo.get_quotes()
edn.get_quotes()
agro.get_quotes()
print("precio " + valo.quotes_data['longName'] + ": ",
valo.quotes_data['regularMarketPrice'])
print("precio " + edn.quotes_data['longName'] + ": ",
edn.quotes_data['regularMarketPrice'])
print("precio " + agro.quotes_data['longName'] + ": ",
agro.quotes_data['regularMarketPrice'])
from pinance import Pinance
import pandas as pd
stock1 = 'HDFC'
stock2 = 'HDFC.NS'
first = Pinance(stock1)
second = Pinance(stock2)
first.get_quotes()
second.get_quotes()
second.get_news()
df1 = pd.DataFrame(data=first.quotes_data, index=[0])
df2 = pd.DataFrame(data=second.quotes_data, index=[0])
df1 = (df1.T)
df2 = (df2.T)
df1.to_excel("DataQuality\hdfc.xls")
df2.to_excel("DataQuality\hdfcNS.xls")
df3 = pd.DataFrame(data=second.news_data, index=[0])
df3 = (df3.T)
df3.to_excel(r"DataQuality\news.xls")
